Valuation Atlas

Associated with the IPBES assessment on the diverse values and valuation of nature and its contributiosn to people

Overview

DOI: 10.5281/zenodo.5524600

The aim was to prepare a spatio-temporal representation of valuation studies related to biodiversity and ecosystem services and … .

1 Creation of the Corpus

1.1 Methodology

1.2 Description of the corpus

2 Georeferencing the Corpus

To identify country names in the corpus of literature a two step approach was used. First, we wanted to understand where studies were conducted and searched the title, abstract, and keywords of each paper for country names. Second, to understand where the funding institutions were located we searched the affiliations, acknowledgments, and funding text for country names.

The input data we used are the following:

• Bib file downloaded from Web of Science

• ISO 3166-1 alpha-3 country (includes some territories) codes (https://www.iso.org/iso-3166-country-codes.html)

• IPBES regional and subregional area dataset (https://doi.org/10.5281/zenodo.3923633)

The python code used to georeference the corpus can be found here. An overview of the pipeline is provided in the following schematic and described below.

knitr::include_graphics("pilot2.svg")

Overview of the process of Georeferencing the corpus of valuation studies

Step 1: Extract country names from text Country names were extracted from the title, abstract, and keywords of each paper with a regular expression and the associated ISO code was added into a a column in the dataset. The same regular expression was also used to search the affiliations, acknowledgments, and funding text of the same paper and placed into a second column.

Step 2 and 3: Bundle countries in regions and subregions The IPBES Regions and Subregions datatset was then used to add additional region and subregion attributes to the dataset by matching the ISO3 code.

Step 4: Find TS accordingly We used a set of files to add additional attributes to the dataset that identified the topics. The set of files contained identifying information for papers derived from sets of web of science searches targeting particular topics. This identifying information was then matched to the corpus, and the topic extracted.

Finally, the complete corpus with the added attributes of country ISO codes of both funding institutions and research locations, and topic identification were used as the basis of the rest of the research project. The complete corpus can be found on Zenodo here: [https://doi.org/[INSERT](https://doi.org/%5BINSERT){.uri} DOI]

3 Valuation Maps

The complete georeferenced valuation corpus was used to understand the location of valuation studies throughout the world and the location of the institutions conducting those studies.

We counted the number of times a country or territory was listed in the corpus from the column of country or territory names obtained from the title, abstract, and keywords search as a proxy for the density of valuation studies.

Separately, we counted the number of times a country or territory was listed in the corpus from the column of country or territory names obtained from the affiliations acknowledgments, and funding text as a proxy for the density of institutions.

3.1 Geographic Distribution

We will now present the results from the analysis through a series of maps showing both the raw country or territory results and summarized by IPBES regions and subregions. No data is always displayed in grey. The darker green values represent higher density of studies, while the darker blue values represent higher density of institutions.

3.1.1 Density of studies

30,428 studies did not identify a country or territory within their title, abstract, or keywords.

All

Showcases the number of valuation studies conducted within each country or territory for the entire dataset. Please note that the scales are not consistent between maps.

There were 64,683 identifications of a country or territory from the title, abstract, or keywords, consisting of 216 countries or territories identified.

The United States has disproportionately higher valuation studies than other countries. In descending order, United States (10.96%), China (6.66%), Australia (5.36%), Brazil (4.66%) and India (3.74%) have the highest valuation studies in total and for studies post 2010.

There was a large relative increase of valuation studies conducted in China before 2010 and after 2010. Before 2010, China comprised of 2.80% of the valuation studies, but after 2010 China comprised 8.04% of the valuation studies.

knitr::include_graphics("Outputs/Maps/Names1_percountry.png")

Before 2010

Showcases the number of valuation studies conducted within each country or territory in the dataset before 2010. Please note that the scales are not consistent between maps.

Before 2010, the countries with the highest valuation studies conducted were USA (12.21%), Australia (6.37%), UK (5.17%), India (3.61%), and Canada (3.32%).

knitr::include_graphics("Outputs/Maps/Names1_percountry_before2010.png")

After 2010

Showcases the number of valuation studies conducted within each country or territory in the dataset from 2010. Please note that the scales are not consistent between maps.

In descending order, United States (10.49%), China (8.04%), Australia (5.18%), Brazil (5.01%) and India (3.80%) have the highest valuation studies in total and for studies post 2010.

knitr::include_graphics("Outputs/Maps/Names1_percountry_2010.png")

3.1.2 Density of institutions

376 studies did not identify a country within their affiliations, acknowledgments, and funding text.

All

Showcases the number of studies which reference the particular country within the affiliations, acknowledgments, or funding text for the entire dataset as a proxy for institutions which are conducing the research. Please note that the scales are not consistent between maps.

There were 140,184 identifications of a country or territory from the affiliations, acknowledgments, or funding text, consisting of 209 countries or territories.

The United States and the United Kingdom (UK) has disproportionately higher density of institutions than other countries. In descending order, the United States (17.89%), UK (7.36%), Germany (5.19%), Australia (4.90%), and China (4.64%) have the highest density of institutions.

knitr::include_graphics("Outputs/Maps/Names2_percountry.png")

Before 2010

Showcases the number of studies which reference the particular country within the affiliations, acknowledgments, or funding text for the studies in the dataset before 2010 as a proxy for institutions which are conducing the research in this time period. Please note that the scales are not consistent between maps.

Before 2010, in descending order, the United States (25.28%), UK (9.52%), Canada (5.23%), Australia (5.03%) and Germany (4.83%) have the highest density of institutions.

knitr::include_graphics("Outputs/Maps/Names2_percountry_before2010.png")

After 2010

Showcases the number of studies which reference the particular country or territory within the affiliations, acknowledgments, or funding text for the studies in the dataset from 2010 as a proxy for institutions which are conducing the research in this time period. Please note that the scales are not consistent between maps.

After 2010, in descending order, the United States (15.81%), UK (6.75%), China (5.39%), Germany (5.29%) and Australia (4.87%) have the highest density of institutions.

knitr::include_graphics("Outputs/Maps/Names2_percountry_2010.png")

3.1.3 Summarized by region

Europe and Central Asia region has the highest density of institutions, but comes in third for the highest density of studies. In contrast, the Americas region comes in first for the highest density of studies and second for institutions.

Besides Antarctica, Africa has the lowest density of studies and institutions.

Density of studies

knitr::include_graphics("Outputs/Maps/Names1_perregion.png")

* 30,428 studies did not have a region identified for the density of studies

Density of institutions

knitr::include_graphics("Outputs/Maps/Names2_perregion.png")

* 376 studies did not have a region identified for the density of institutions

3.1.4 Summarized by subregion

Europe and North America disproportionately have the highest density of studies and institutions between subregions, while Central Asia have the lowest density for both categories.

Density of studies

knitr::include_graphics("Outputs/Maps/Names1_persubregion.png")

* 30,428 studies did not have a subregion identified for the density of studies

Density of institutions

knitr::include_graphics("Outputs/Maps/Names2_persubregion.png")

* 376 studies did not have a subregion identified for the density of institutions

3.2 Relationships between density of studies and institutions

It is clear that there is a significant positive linear relationship between the density of studies and institutions. It is important to note that there were many more identified countries for institutions than location of studies.

knitr::include_graphics("Outputs/Corpus/Names1_Names2.png")

Density of studies vs density of institutions

3.3 Temporal Trends

There has been an exponential increase in valuation studies from the 1980’s to the present as pictured in the graph below.

knitr::include_graphics("Outputs/Corpus/Timeline.svg")

• 569 studies did not have a year identified
• Data from 2020 was removed due to incomplete sampling of 2020.

3.3.1 Geographic density of studies

Additionally, we explored how the density of valuation studies has changed throughout the last four decades. No data is displayed in grey.

Pre 1990

Pre 1990 within the corpus, there are four countries represented (USA, Brazil, Norway, and Sweden) with a total of 5 identifications of a country or territory. All countries had only one identification, except for the USA with 2.

knitr::include_graphics("Outputs/Maps/temporal/Names1_1980-1990.png")

1990 - 2000

Between 1990 and 2000 within the corpus, there are 153 countries or territories represented with a total of 2,704 identifications, three orders of magnitude larger than the number of identifications before 1990.

knitr::include_graphics("Outputs/Maps/temporal/Names1_1990-2000.png")

2000 - 2010

Between 2000 and 2010 within the corpus, there are 200 countries or territories represented with a total of 14,644 identifications, again an order of magnitude increase from before 2000.

knitr::include_graphics("Outputs/Maps/temporal/Names1_2000-2010.png")

Post 2010

2010 and later within the corpus, there are 214 countries or territories represented with a total of 46,918 identifications.

knitr::include_graphics("Outputs/Maps/temporal/Names1_2010-2020.png")

3.3.2 Geographic density of institutions

Additionally, we explored how the density of valuation institutions has changed through time throughout the last four decades. No data is displayed in gray.

Pre 1990

Pre 1990 within the corpus, there are eight countries or territories represented with a total of 24 identifications of a country or territory. USA had 14 identifications, Canada 3 identifications, Norway 2 identifications, while the rest of the represented countries had 1.

knitr::include_graphics("Outputs/Maps/temporal/Names2_1980-1990.png")

1990 - 2000

Between 1990 and 2000 within the corpus, there are 110 countries or territories represented with a total of 4,461 identifications of a country or territory, two orders of magnitude larger than pre 1990.

knitr::include_graphics("Outputs/Maps/temporal/Names2_1990-2000.png")

2000 - 2010

Between 2000 and 2010 within the corpus, there are 179 countries or territories represented with a total of 26,476 identifications of a country or territory, an order of magnitude larger than pre 2000.

knitr::include_graphics("Outputs/Maps/temporal/Names2_2000-2010.png")

Post 2010

2010 and later within the corpus, there are 207 countries or territories represented with a total of 108,101 identifications of a country or territory.

knitr::include_graphics("Outputs/Maps/temporal/Names2_2010-2020.png")

4 Indicator Analysis

The IPBES Core Indicators were used alongside a chosen set of other relevant indicators to understand geographic trends between density of valuation studies and how they relate to biological and socioeconomic indicators.

4.1 Selection of Indicators

We used all the most recent year of the IPBES Core Indicators available within the country dataset except for two indicators, Countries/Regions with Active NBSAP and Category 1 nations in CTIES, as these are binary in the dataset and would not be compatible with the following analysis. We selected a specific category from the indicators with multiple categories. For example, for the indicator “Area of forest production under FSC and PEFC certification” we chose the FSC certification area and not the PEFC certification area.

A set of other indicators were included in the analysis to expand the coverage of socioeconomic variables. We included the human development index (HDI), average harmonized learning outcomes score, gross domestic product (GDP), corruption perception index (CPI), and population.

These datasets were downloaded, cleaned, and had ISO3 codes added to easily merge them into the analysis. The latest data available was used for each indicator.

Here is the table of all of the indicators used, the category selected, the year the data is from, and the number assigned to them.

Table 1: Details of the Indicators Used

Name	Category	Year
Area of forest production under FSC and PEFC certification	FSC_area	2016
Biodiversity Habitat Index	Average	2014
Biodiversity Intactness Index	Value	2005
Biocapacity per capita	Value - Total	2012
Ecological Footprint per capita	Value - Total	2012
Forest area	Forest area (1000ha)	2015
Water Footprint	Water Footprint - Total (Mm3/y)	2013
Inland Fishery Production	Capture	2015
Region-based Marine Trophic Index	1950	2014
Nitrogen + Phosphate Fertilizers	N total nutrients - Consumption in nutrients	2014
Nitrogen Use Efficiency (%)	Nitrogen Use Efficiency (%)	2009
Percentage and total area covered by protected areas	Terrestrial - Protected Area (%)	2017
Percentage of undernourished people	Prevalence of undernourishment (%) (3-year average)	2015
Proportion of local breeds, classified as being at risk, not-at-risk or unknown level of risk of extinction	At Risk of Extinction	2016
PA of Key Biodiversity Areas Coverage (%)	Estimate	2016
Protected area management effectiveness	PA Assessed on Management Effectiveness (%)	2015
Protected Area Connectedness Index	Protected Area Connectedness Index	2012
Species Habitat Index	Species Habitat Index	2014
Species Protection Index (%)	Species Protection Index (%)	2014
Species Status Information Index	Value	2014
Total Wood Removals (roundwood, m3)	Total	2014
Trends in forest extent (tree cover)	Percentage of Tree Cover Loss	2015
Nitrogen Deposition Trends (kg N/ha/yr)	Nitrogen Deposition Trends (kg N/ha/yr)	2030
Trends in Pesticides Use	Use of pesticides (3-year average)	2013
Human Development Index (HDI)	NA	2018
Average harmonized learning outcomes score	NA	2015
Gross domestic product (GDP)	NA	2019
Corruption perception index (CPI)	NA	2020
Population	NA	2018

There were a few instances of duplicated values which were double checked with the original dataset and the erranous value removed. Examples include having two values for USA due to the separation of Hawaii in the original dataset. In these cases Hawaii was removed and the value referring to the rest of the states of the country was used instead. Additionally, Indicator 9, Region-based Marine Trophic Index, the mean of the regions was calculated per country, as countries such as Germany have multiple regions with distinct values.

4.2 Dataset Compilation

To understand how valuation is spread across geographies, we counted the number of times each country’s or terriroies’ ISO code appeared in the corpus for both geography columns added in step 2. The result is the density of studies per country or terriroty and the density of funding institutions per country or territory for the entire corpus.

The external indicators were also joined onto the dataset to analyze the relationships between these socioeconomic indicators and the density of studies and funding institutions.

This process was also repeated with an additional filter that excluded any studies published before 2010.

4.3 Pearson Correlation Analysis

To investigate the relationships between indicators and the number of valuation studies, we ran a pearson correlation analysis. The statistical analysis calculated the trends between the number of studies in each country or territory (Density of studies) and the number of studies funded in each country or territory (Density of institutions) and each of the indicators. The results are shown below.

Insignificant relationships are blank, significant relationships (P < 0.01) are shown with circles. The strength of the correlation corresponds to the size of the circle and the color represents positive (blue) or negative (red) trends.

Description of results

There is a very strong positive relationship between GDP and the density of both studies and institutions. GDP per capita does not exhibit the same strong correlation.

Density of studies is also strongly positively correlated to total wood removals, trends in pesticide use, population, nitrogen fertilizers, and water footprint index.

Density of institutions is also strongly positively correlated with total wood removals, and nitrogen fertilizers.

While most significant relationships are positive, there are two negative relationships present. Biodiversity inexactness index is negatively correlated with both the density of studies and institutions, and protected area management effectiveness is slightly negative correlated with the density of institutions.

knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure.png")

Pearson correlations of geographic valuation studies and indicators

The same pearson correlation analysis with the indicators was conducted on the log of the number of studies in each country or territory and the log of the number of studies funded from each country.

Description of results

Generally, the log density of studies and institutions show stronger and more correlations with the indicators. Within the log relationship, the protected area management effectiveness becomes significantly negatively correlated with both the density of studies and institutions. The biodiversity inexactness index also follows the same pattern.

For the log density of studies compared to the non-transformed density of studies, GDP becomes less strongly correlated, local breeds at risk of extinction becomes positively correlated, protected area connectedness index becomes strongly positively correlated, species protection index becomes positively correlated, as well as trends in forest extent, and nitrogen deposition trends.

For the log density of institutions compared to the non-transformed density of institutions, the human development index, average harmonized learning outcomes, corruption perception index, population, and local breeds at risk of extinction all become more strongly positively correlated, while the GDP becomes slightly less positively correlated. Additionally, the percentage of undernourished people becomes negatively correlated with the log density of studies, the species protection index and the species status information index become positively correlated, and finally the relationship to trends in pesticide use is no longer significant.

For both the log density of studies and institutions, the protected area connected index becomes strongly positively corrrelated.

knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure_log.png")

Pearson correlations of log geographic valuation studies and the indicators

4.4 Individual Trend Analysis

The individual trends between each indicator and the number of studies and number of funding institutions are shown here for the entire corpus. For each indicator, there are two four panel figures with the top figure showing the trends with the raw values, and the bottom figure displaying the trends with log transformed x-axis values.

The dots represent countries within the dataset that have values for both the indicator and valuation atlas. The associated p value of the linear trend model is shown in the corner of the image in red. The trend line is shown in blue and the associated standard error in grey. Please note that the y axis is not necessarily consistent between the four boxes and may include values that aren’t actually found in the data to showcase the full extent of the standard error of the trend line.

For each figure the panels are arranged accordingly:

• Names 1 (top-left) is the number of valuation studies for each country or territory in the corpus

• Names 1 from 2010 (top-right) is the number of valuation studies for each country or territory in the corpus from 2010 to 2020

• Names 2 (bottom-left) is the number of valuation studies funded from each country or territory in the corpus

• Names 2 from 2010 (bottom-right) is the number of valuation studies funded from each country or territory in the corpus from 2010 to 2020

Human Development Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/HDI.svg")

Relationships between valuation atlas and the human development index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/HDI_log.svg")

Log relationships between valuation atlas and the human development index

Learning Outcomes

knitr::include_graphics("Outputs/Figures_Corpus_individual/LearningOutcomes.svg")

Relationships between valuation atlas and learning outcomes

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/LearningOutcomes_log.svg")

Log relationships between valuation atlas and learning outcomes

GDP

knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP.svg")

Relationships between valuation atlas and GDP

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_log.svg")

Log relationships between valuation atlas and GDP

Corruption Perception Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/CPI.svg")

Relationships between valuation atlas and the corruption perception index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/CPI_log.svg")

Log relationships between valuation atlas and the corruption perception index

Population

knitr::include_graphics("Outputs/Figures_Corpus_individual/POP.svg")

Relationships between valuation atlas and population

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/POP_log.svg")

Log relationships between valuation atlas and population

GDP per capita

knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_per_capita.svg")

Relationships between valuation atlas and GDP per capita

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_per_capita_log.svg")

Log relationships between valuation atlas and GDP per capita

Forest area under FSC certification

knitr::include_graphics("Outputs/Figures_Corpus_individual/FSC.svg")

Relationships between valuation atlas and forest area under FSC certification

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/FSC_log.svg")

Log relationships between valuation atlas and forest area under FSC certification

Biodiversity Habitat Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_habitat_index.svg")

Relationships between valuation atlas and the biodiversity habitat index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_habitat_index_log.svg")

Log relationships between valuation atlas and the biodiversity habitat index

Biodiversity Intactness Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_intactness_index.svg")

Relationships between valuation atlas and the biodiversity intactness index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_intactness_index_log.svg")

Log relationships between valuation atlas and the biodiversity intactness index

Biocapacity per capita

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biocapacity_per_capita.svg")

Relationships between valuation atlas and the biocapacity per capita

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Biocapacity_per_capita_log.svg")

Log relationships between valuation atlas and the biocapacity per capita

Ecological Footprint per capita

knitr::include_graphics("Outputs/Figures_Corpus_individual/Ecological_footprint.svg")

Relationships between valuation atlas and the ecological footprint per capita

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Ecological_footprint_log.svg")

Log relationships between valuation atlas and the ecological footprint per capita

Forest area

knitr::include_graphics("Outputs/Figures_Corpus_individual/Forest_area.svg")

Relationships between valuation atlas and forest area

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Forest_area_log.svg")

Log relationships between valuation atlas and forest area

Water Footprint

knitr::include_graphics("Outputs/Figures_Corpus_individual/Water_footprint.svg")

Relationships between valuation atlas and the water footprint

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Water_footprint_log.svg")

Log relationships between valuation atlas and the water footprint

Inland Fishery Production

knitr::include_graphics("Outputs/Figures_Corpus_individual/Inland_fishery_production.svg")

Relationships between valuation atlas and inland fishery production

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Inland_fishery_production_log.svg")

Log relationships between valuation atlas and inland fishery production

Region-based Marine Trophic Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Marine_tropic_index.svg")

Relationships between valuation atlas and the region-based marine trophic index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Marine_tropic_index_log.svg")

Log relationships between valuation atlas and the region-based marine trophic index

Nitrogen Fertilizers

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_fertilizers.svg")

Relationships between valuation atlas and nitrogen fertization

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_fertilizers_log.svg")

Log relationships between valuation atlas and nitrogen fertization

Nitrogen Use Efficiency (%)

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_use_efficiency.svg")

Relationships between valuation atlas and nitrogen use efficiency

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_use_efficiency_log.svg")

Log relationships between valuation atlas and nitrogen use efficiency

Percentage of area covered by protected areas

knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_protected.svg")

Relationships between valuation atlas and percentage of area covered by protected areas

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_protected_log.svg")

Log relationships between valuation atlas and percentage of area covered by protected areas

Percentage of undernourished people

knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_of_undernourished_people.svg")

Relationships between valuation atlas and percentage of undernourished people

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_of_undernourished_people_log.svg")

Log relationships between valuation atlas and percentage of undernourished people

Local Breeds at risk of extinction

knitr::include_graphics("Outputs/Figures_Corpus_individual/Local_breeds.svg")

Relationships between valuation atlas and local breeds at risk of extinction

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Local_breeds_log.svg")

Log relationships between valuation atlas and local breeds at risk of extinction

PA of Key Biodiversity Areas Coverage (%)

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_of_key_biodiversity_area_coverage.svg")

Relationships between valuation atlas and PA of key biodiverisy area coverage

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_of_key_biodiversity_area_coverage_log.svg")

Log relationships between valuation atlas and PA of key biodiverisy area coverage

Protected area management effectiveness

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_management_effectiveness.svg")

Relationships between valuation atlas and protected area management effectiveness

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_management_effectiveness_log.svg")

Log relationships between valuation atlas and protected area management effectiveness

Protected Area Connectedness Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_connectedness.svg")

Relationships between valuation atlas and the protected area connectedness index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_connectedness_log.svg")

Log relationships between valuation atlas and the protected area connectedness index

Species Habitat Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_habitat_index.svg")

Relationships between valuation atlas and the species habitat index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_habitat_index_log.svg")

Log relationships between valuation atlas and the species habitat index

Species Protection Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/species_protection_index.svg")

Relationships between valuation atlas and the species protection index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/species_protection_index_log.svg")

Log relationships between valuation atlas and the species protection index

Species Status Information Index

knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_status.svg")

Relationships between valuation atlas and the species status information index

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_status_log.svg")

Log relationships between valuation atlas and the species status information index

Total Wood Removals (roundwood, m3)

knitr::include_graphics("Outputs/Figures_Corpus_individual/Total_wood_removals.svg")

Relationships between valuation atlas and total wood removals

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Total_wood_removals_log.svg")

Log relationships between valuation atlas and total wood removals

Trends in forest extent (tree cover)

knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_forest_extent.svg")

Relationships between valuation atlas and trends in forest extent

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_forest_extent_log.svg")

Log relationships between valuation atlas and trends in forest extent

Nitrogen Deposition Trends

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends.svg")

Relationships between valuation atlas and nitrogen deposition trends

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends_log.svg")

Log relationships between valuation atlas and nitrogen deposition trends

Nitrogen Deposition Trends (kg N/ha/yr)

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends.svg")

Relationships between valuation atlas and nitrogen deposition trends

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends_log.svg")

Log relationships between valuation atlas and nitrogen deposition trends

Trends in Pesticides Use

knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_pesticides_use.svg")

Relationships between valuation atlas and trends in pesticides use

Log relationship graphs:

knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_pesticides_use_log.svg")

Log relationships between valuation atlas and trends in pesticides use